188 lines
7.1 KiB
C++
188 lines
7.1 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/delegates/xnnpack/pad_tester.h"
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#include <array>
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#include <cstdint>
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#include <functional>
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#include <numeric>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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namespace tflite {
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namespace xnnpack {
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std::vector<int32_t> PadTester::OutputShape() const {
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std::vector<int32_t> output_shape;
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output_shape.reserve(InputShape().size());
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for (size_t i = 0; i < InputShape().size(); i++) {
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int32_t output_dim = InputShape()[i];
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if (i < InputPrePaddings().size()) {
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output_dim += InputPrePaddings()[i];
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}
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if (i < InputPostPaddings().size()) {
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output_dim += InputPostPaddings()[i];
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}
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output_shape.push_back(output_dim);
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}
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return output_shape;
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}
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void PadTester::Test(TfLiteDelegate* delegate) const {
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ASSERT_EQ(InputPrePaddings().size(), InputPostPaddings().size());
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ASSERT_LE(InputPrePaddings().size(), InputShape().size());
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto input_rng =
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std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
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std::vector<char> buffer = CreateTfLiteModel();
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const Model* model = GetModel(buffer.data());
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std::unique_ptr<Interpreter> delegate_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(model, ::tflite::ops::builtin::BuiltinOpResolver())(
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&delegate_interpreter),
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kTfLiteOk);
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std::unique_ptr<Interpreter> default_interpreter;
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ASSERT_EQ(
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InterpreterBuilder(model, ::tflite::ops::builtin::BuiltinOpResolver())(
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&default_interpreter),
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kTfLiteOk);
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ASSERT_TRUE(delegate_interpreter);
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ASSERT_TRUE(default_interpreter);
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ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
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ASSERT_EQ(default_interpreter->inputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
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ASSERT_EQ(default_interpreter->outputs().size(), 1);
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ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);
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float* default_input_data = default_interpreter->typed_tensor<float>(
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default_interpreter->inputs()[0]);
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std::generate(default_input_data,
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default_input_data + ComputeSize(InputShape()),
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std::ref(input_rng));
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float* delegate_input_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->inputs()[0]);
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std::copy(default_input_data, default_input_data + ComputeSize(InputShape()),
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delegate_input_data);
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ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
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ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);
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float* default_output_data = default_interpreter->typed_tensor<float>(
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default_interpreter->outputs()[0]);
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float* delegate_output_data = delegate_interpreter->typed_tensor<float>(
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delegate_interpreter->outputs()[0]);
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for (size_t i = 0; i < ComputeSize(OutputShape()); i++) {
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ASSERT_EQ(default_output_data[i], delegate_output_data[i]);
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}
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}
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std::vector<char> PadTester::CreateTfLiteModel() const {
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flatbuffers::FlatBufferBuilder builder;
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flatbuffers::Offset<OperatorCode> operator_code =
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CreateOperatorCode(builder, BuiltinOperator_PAD);
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std::vector<int32_t> paddings(InputPrePaddings().size() +
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InputPostPaddings().size());
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for (size_t i = 0; i < InputPrePaddings().size(); i++) {
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paddings[i * 2] = InputPrePaddings()[i];
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paddings[i * 2 + 1] = InputPostPaddings()[i];
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}
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const std::array<flatbuffers::Offset<Buffer>, 2> buffers{{
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CreateBuffer(builder, builder.CreateVector({})),
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CreateBuffer(builder,
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builder.CreateVector(
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reinterpret_cast<const uint8_t*>(paddings.data()),
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sizeof(float) * paddings.size())),
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}};
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const std::vector<int32_t> output_shape = OutputShape();
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const std::array<int32_t, 2> paddings_shape{
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{static_cast<int32_t>(InputPrePaddings().size()), 2}};
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const std::array<flatbuffers::Offset<Tensor>, 3> tensors{{
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CreateTensor(builder,
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builder.CreateVector<int32_t>(InputShape().data(),
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InputShape().size()),
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TensorType_FLOAT32),
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CreateTensor(builder,
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builder.CreateVector<int32_t>(paddings_shape.data(),
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paddings_shape.size()),
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TensorType_INT32, /*buffer=*/1),
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CreateTensor(builder,
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builder.CreateVector<int32_t>(output_shape.data(),
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output_shape.size()),
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TensorType_FLOAT32),
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}};
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const std::array<int32_t, 2> op_inputs{{0, 1}};
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const std::array<int32_t, 1> op_outputs{{2}};
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flatbuffers::Offset<Operator> op = CreateOperator(
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builder, /*opcode_index=*/0,
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builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
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builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
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const std::array<int32_t, 1> subgraph_inputs{{0}};
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const std::array<int32_t, 1> subgraph_outputs{{2}};
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flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
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builder, builder.CreateVector(tensors.data(), tensors.size()),
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builder.CreateVector<int32_t>(subgraph_inputs.data(),
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subgraph_inputs.size()),
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builder.CreateVector<int32_t>(subgraph_outputs.data(),
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subgraph_outputs.size()),
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builder.CreateVector(&op, 1));
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flatbuffers::Offset<flatbuffers::String> description =
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builder.CreateString("Pad model");
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flatbuffers::Offset<Model> model_buffer = CreateModel(
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builder, TFLITE_SCHEMA_VERSION, builder.CreateVector(&operator_code, 1),
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builder.CreateVector(&subgraph, 1), description,
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builder.CreateVector(buffers.data(), buffers.size()));
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builder.Finish(model_buffer);
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return std::vector<char>(builder.GetBufferPointer(),
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builder.GetBufferPointer() + builder.GetSize());
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}
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int32_t PadTester::ComputeSize(const std::vector<int32_t>& shape) {
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return std::accumulate(shape.cbegin(), shape.cend(), 1,
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std::multiplies<int32_t>());
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}
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} // namespace xnnpack
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} // namespace tflite
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